基于改进S 变换和贝叶斯相关向量机的电能质量扰动识别

提出一种改进S变换和相关向量机相结合的电能质量扰动分类法.首先通过引入调节因子构建时频分辨率可控的改进S变换,从而提取各类扰动信号的时频特性;然后利用层次分类法与最小输出编码法构建贝叶斯相关向量机多级分类树模型,实现电能质量扰动信号的分类与识别.研究表明,该方法能在强噪声背景下获得高精度的扰动分类识别率,具备比S变换更高的时频分析能力,较支持向量机需要更少的相关向量数目,测试时间更短.

Classification identification of power quality disturbances based on modified S-transform and Bayes relevance vector machine

A method classifying power quality disturbances(PQD) based on modified S-transform and relevance vector
machine(RVM) is presented. The modified S-transform(MST) is achieved by adding three adjustable factors to the
Gaussian window function of the normal S-Transform. The adjustable factors change the velocity in which the width of
the window function varies with the frequency. The PQD sample eigenvectors can be extracted accurately by using the
modified S-transform with better time-frequency analysis performance than the S-Transform. Then the disturbance types
are identified through the multi-lay RVM pattern recognition classifier on hierarchical categorization and minimum output
coding. Numerical results show that the proposed MST-based RVM method can achieve higher classification accuracy
quickly, and requires substantially fewer relevance vectors and shorter test time than the SVM classifier.